Launching today
Unlike generic crypto research assistants, Fere turns market signals into autonomous trading workflows. Agents research opportunities, build trade setups, optimize routes and fees, execute with a wallet, and monitor strategies 24/7 across crypto and Polymarket. Standout features include autonomous Polymarket trading, entry/exit rules, stop-loss controls, execution routing, and lower-cost agent runs.
Fere AI
"Your AI should be making your trades, not just narrating them."
That line wouldn't leave us alone. So here we are.
I'm Aron. Pranav and I have been building autonomous AI since 2014, before agents were a buzzword. Enterprise AI for pharma, Fortune 100 ops, web3 infra. Multiple exits. A few brutal failures. One obsession throughout: AI that actually acts, not just answers.
Crypto handed us the perfect environment. But the workflow was broken. I was bouncing between six tabs every morning, and every "AI" I tried would research beautifully then hand me back the mouse. Not an agent. A fancy search engine.
The market splits into two failures:
Chatbots that walk you through a trade and never make it
Bots that fire orders all day and can't tell you why
Either way, you end up doing the work anyway. Fere is the third thing.
Tell it your thesis in plain English. It researches, trades, manages risk — with its own wallet, across multiple chains, for days unattended.
What you can hand it today:
→ "Track top 5 AI tokens by 7-day volume. Rebalance weekly. Cut anything down 20%, let winners run."
→ "Find me easy wins on polymarket"
→ "Every day buy 10$ of eth for me as long as it is under 2400$"
Why it works: most "AI agents" tap out after one prompt. Ours have been live 90+ days straight — reasoning, remembering, adapting, improving. Not scripts with vibes. Real system underneath: planner, retriever, analyst, executor, guardian.
Where we are: 7,000+ daily users. 10M+ autonomous executions. Backed by Ethereal Ventures, Galaxy Vision Hill, and Kosmos Ventures.
We're just getting started. Swarm framework goes open-source next. The agentic internet is coming — we're building the infrastructure early.
Try free at fereai.xyz , no card needed.
What's the trade or thesis you'd actually trust an AI to run? Drop it below 👇 Pranav and I are here all day.
~ Aron & Pranav
Building agents myself, the hardest unsolved problem isn't capability; it's reliability over long horizons.
What's your approach to handling task drift and error recovery in multi-step flows? This is where most agent products silently fail.
Fere AI
@ishu86 This is the right question and most people are not asking it yet.
Task drift and silent failure in multi-step flows is genuinely the hardest infrastructure problem we deal with. Our approach has three layers.
First, specialist sub-agents. Instead of one general agent trying to hold context across a long horizon, each sub-agent owns a narrow, well-defined task. Smaller scope means less surface area for drift.
Second, ordered task execution with checkpoints. Fere breaks a strategy into a sequence of atomic tasks. Each step validates its own output before passing to the next. If something looks off, it does not silently proceed.
Third, live market feedback as a correction signal. Because our agents operate with real wallets in real markets, they get continuous ground truth. Reinforcement learning against live outcomes means errors surface fast and the agent self-corrects over time rather than compounding mistakes.
We will not claim we have fully solved this. Nobody has. But 10 million live executions across 90 plus day strategies gives us a real feedback loop that most agent products running on synthetic benchmarks simply do not have.
Would love to compare notes on what you are seeing on your end.
@abh3nav Agree on decomposition and checkpoints. Want to press on "RL against live outcomes" though, since that phrase covers four very different systems:
Gradient-based online weight updates from live rewards (hard behind closed APIs, risky on open-weights with real capital).
Gradient-based offline fine-tuning on aggregated rewarded trajectories.
Gradient-free in-context learning, past outcomes retrieved into the prompt.
Gradient-free bandit or search over a frozen-weight strategy space.
Each has very different scaling properties and failure modes. Which is Fere actually running? 10M executions is a great corpus for (2) and (3), but it is a different system from a self-correcting policy in the RL sense.
Fere AI
@ishu86 Fair challenge, and you are right to press on it.
We are not running gradient-based online weight updates in production with live capital. That would be reckless and we will not pretend otherwise. What we are running is closest to a combination of (3) and (4) with a pathway toward (2).
In practice: past execution trajectories, including outcomes, slippage, timing errors, and strategy drift signals, are retrieved into context to inform current agent decisions.
Simultaneously, we are running strategy-level selection across a frozen-weight space, where the bandit logic scores and routes to approaches that have performed better on similar market conditions historically.
The 10M execution corpus is exactly what feeds this. We are actively building the offline fine-tuning pipeline on top of it, which is where (2) comes in over time.
Where we are careful with language: self-improving in our context means the system makes better decisions as the corpus grows, not that weights are updating in real time. That distinction matters and we should probably be more precise about it publicly.
This is a genuinely useful push. The RL framing in AI products is often loose and we are not immune to that. Appreciate you making us be specific.
Fere AI
@lakshminath_dondeti You’re right that some frontier model providers’ terms/policies treat finance as a sensitive or high-risk domain. But I don’t think the clean reading is “financial or crypto research / trade is prohibited.”
OpenAI’s policy calls out “tailored advice that requires a license” and “automation of high-stakes decisions” in “financial activities and credit,” not financial research broadly. Anthropic is even more explicit: finance is a “High-Risk Use Case,” including investment advice and financial eligibility/creditworthiness, but the requirement is human review and disclosure rather than a blanket ban. Google’s policy has the same general shape around high-risk finance decisions.
Our product follows all standard guidelines for such high risk use case, including disclosures, risk understandings and warnings at necessary steps.
Regarding our choice of models - It varies. We do use the top models in both open and closed weights across different places.
@lakshminath_dondeti @pranavprakash Fair on the policy reading - finance is high-risk-with-conditions, not banned. But the more interesting question your "across different places" answer skips: where in the loop does the actual trading decision get made? An agent that uses closed-weights for research/summarization but open-weights for the trade signal is a very different product from one that pipes positions and signals into a third-party API. The first owns its alpha. The second rents it. Which one is Fere?
Swach Discord
Nice, how does it handle flash crashes or black swans?
BirdTab
Fere AI
@jaipandya Thanks a ton!
ZeroHuman.
Excited to hunt Fere AI today!
I'm impressed by Fere AI's execution-first approach to agentic finance: turning market signals into crypto and Polymarket trades, not just research reports.
What I particularly appreciate is the focus on the full workflow: research the opportunity, build the setup, optimize routes and fees, and execute with a wallet.
The Polymarket angle also makes this stand out. Most crypto agents compete on research, but Fere is pushing further into autonomous execution.
Also: cheaper query + execution runs make the product feel practical.